In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%.

翻译：在快速发展的下一代网络（NGN）时代，采用零信任架构对于保障安全变得日益关键。然而，在NGN中提供零信任服务面临重大挑战，主要源于环境复杂性和动态性。受这些挑战驱动，本文探索利用分层微分割实现高效的零信任服务供给。具体而言，我们通过分层图对零信任网络进行建模，从而联合考虑资源与信任层级特征以优化服务效率。我们通过微分割组织此类零信任网络，其能高效支持细粒度零信任策略。为生成最优微分割，我们提出了大语言模型增强图扩散（LEGD）算法，该算法利用扩散过程实现高质量生成范式。此外，我们运用策略增强和大语言模型（LLM）使LEGD能够优化生成策略并理解复杂的图特征。进一步地，针对零信任NGN中特有的可信度更新或服务升级需求，我们提出了LEGD自适应维护（LEGD-AM），为LEGD提供面向任务的自适应微调方法。大量实验表明，所提出的LEGD在提供服务方面的效率较其他基线方法提升90%。此外，LEGD-AM能将服务中断时间降低50%以上。